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Electronic health records can help select candidates for HIV PrEP
Liz Highleyman, 2016-11-01 11:50:00
A machine learning algorithm used to analyse
electronic health records (EHRs) identified high-risk individuals who could
potentially benefit from HIV pre-exposure prophylaxis (PrEP), according to a
report presented this week at IDWeek 2016 in New Orleans. Out of 800,000
patients in a large EHR database, more that 8000 were found to be potential
The US Food and Drug Administration (FDA) approved Truvada (tenofovir/emtricitabine) for
HIV prevention in July 2012. Studies of gay and bisexual men have shown that
PrEP reduces the risk of acquiring HIV by more than 90% if used consistently,
with no new infections among people who took it at least four times a
PrEP use has accelerated rapidly in recent years as
clinical trials and demonstration projects continue to confirm its safety and
efficacy. It has been difficult to estimate
how many people have used PrEP because this information is not centrally
collected. A recent survey of
retail pharmacies by Gilead Sciences found that more than 79,000 people in the U.S. have taken PrEP over the past four years. But the Centers for Disease Control and
Prevention estimates that more than 1.2 million people could potentially benefit from PrEP,
including a quarter of sexually active gay and bisexual men.
Douglas Krakower of Beth Israel Deaconess Medical
Center described an effort to develop an automated algorithm to identify people
at increased risk for acquiring HIV using routinely collected information from
electronic health records. This study was
selected as a featured abstract by the HIV Medicine Association, one of the four
infectious disease societies that sponsor IDWeek.
One of the major barriers to getting more people on
PrEP is not having sexual health and risk evaluations done as part of routine
care, as providers have competing demands and may lack the training and comfort
to discussions sexual health with their patients, Dr Krakower noted as
The effort involved three steps. The researchers first extracted
potentially relevant data from the electronic health records of Atrius Health,
a large group practice in the Boston area with approximately 800,000 patients.
They looked at more than 100 variables including patient demographics, recorded
diagnoses, medication prescriptions, laboratory tests and procedures.
The team then matched each of the 138 patients who became infected with
HIV during 2006-2015 to 100 control subjects of the same sex and similar
duration of Atrius Health membership who remained HIV-negative, comparing their
characteristics and risk factors.
They next used logistic regression modelling and machine learning to
predict incident HIV infections among case versus control patients. Logistic
regression is a more traditional approach that makes assumptions about what the
data will look like, Krakower explained, while machine learning is a new
approach in which the computer learns to recognise patterns in the data that
may not have been apparent at the outset.
Finally, the researchers looked at whether the distribution of HIV
prediction scores in the Atrius Health general population could point to a
sub-population who might be candidates for PrEP.
In a comparison of computer algorithms to each other and to logistic
regression, several machine learning methods did a good job – better than
logistic regression – at predicting incident HIV infection. One method known as
Ridge Regression demonstrated the best predictive performance (AUC = 0.76), and
the LASSO method also performed well.
Looking more closely at a few of the variables associated with HIV risk,
6.5% of people who became infected with HIV had undergone anal cytology
testing, compared to less than 0.1% of uninfected control subjects. Similarly, 3.6%
of people with HIV had received a recent prescription for benzathine penicillin G (Bicillin) – used to treat syphilis – compared to less than
0.1% of uninfected controls. And 5.8% of people with HIV had ever had a
positive gonorrhoea test, compared to less than 0.1% among control subjects.
The vast majority of members had risk scores indicating they were at
very low or low risk of HIV infection. However, after excluding people who were
already HIV-positive or currently receiving PrEP, the algorithm identified 8414
individuals – 1.1% of the general population – as potential PrEP
"When you see 8000 patients, that's a lot to
think about providing PrEP to, but if you have a primary care provider handling
1000 patients, this 1.1% would represent 11 of their patients," Dr Krakower
said. "I think this is a clinically reasonable and manageable sub-group of
the population for more intensive screening."
The investigators concluded that automated analysis of data routinely
stored in electronic heath records can identify patients at increased risk for
HIV who are potential candidates for PrEP.
They next plan to optimise the predictive algorithm and validate it with
patients at Fenway Health in Boston, a community health centre that specialises
in care for sexual and gender minorities where PrEP use is much more common.
They then hope to conduct a pilot study with clinicians to see if the algorithm
leads to increased appropriate use of PrEP in a real-world setting.